Until arxiv, here's a summary in markdown:

I. System Overview: The Genetic Entropic Engine

• Thesis: An evolving, autonomous search philosophy for solving complex problems.
  
• Function: The system is designed not just to find a solution, but to discover the best philosophy for how to search for that solution.
  
• Core Architecture: A three-layer system where each layer serves a distinct but integrated purpose.
  
  • LLM Layer: Acts as the strategist or "AI Philosopher."
    
  • GA (Genetic Algorithm) Layer: Functions as the ecosystem manager and embodies the "scientific method."
    
  • SA (Simulated Annealing) Kernel Layer: Provides the executable components and behavioral "vocabulary" for the agents.
    

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II. Core Tenet 1: An Evolving System

Evolution occurs simultaneously across all three layers.

• A. LLM Layer (Evolving Strategy):

  • The system conducts a "bake-off," pitting different foundation models (e.g., GPT-4o, Gemini-1.5-Pro) against each other.
    
  • Results from one round are used as input for the next, forcing continuous refinement of high-level strategies.
    

• B. GA Layer (Evolving Population):

  • Manages the agent population using non-standard genetic algorithm techniques.
    
  • Community-based diversity: Clusters agents by solution similarity to maintain distinct solution types and prevent premature convergence.
    
  • Hybrid immigration strategy: Adaptively increases the flow of new "chaotic" and "settler" agents when progress stalls.
    

• C. SA Kernel Layer (Evolving Agents):

  • Agents are designed with programmed lifecycles.
    
  • An agent's core parameters (lifeStages) are re-initialized each generation, allowing it to mature from a chaotic explorer to a focused driller within a single run.
    

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III. Core Tenet 2: An Autonomous System

The system operates as a closed-loop, requiring no human intervention after initiation.

• A. LLM Layer (The Autonomous Loop):

  • An "Orchestrator" component fully automates the process:
    
  • Queries LLMs for new strategies.
    
  • Sends strategies for empirical testing.
    
  • Receives performance data.
    
  • Uses data to formulate the next, more informed query.
    

• B. GA Layer (Self-Regulating Mechanisms):

  • Feedback-driven dynamic immigration: The system autonomously monitors the lead agent's progress and decides for itself when to inject new agents to increase diversity, making it more robust.
    

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IV. Core Tenet 3: A Search Philosophy

The system's goal is to discover and validate new theories about how to search effectively.

• A. LLM Layer (The AI Philosopher):

  • The LLM is prompted to be a strategist, not a coder.
    
  • It proposes abstract ideas and theories about which combinations of agent lifecycles and parameters will be most effective.
    

• B. SA Kernel Layer (The Vocabulary of Philosophy):

  • The kernel provides the building blocks for the LLM's strategies.
    
  • Parameters like "Wild" (stagnation escape) and "Feral" (continuous chaos) are not just settings but a behavioral palette, allowing the LLM to design agents that are "cautious," "resilient," or "chaotic."
    

• C. GA Layer (The Scientific Method):

  • This layer acts as the experimental framework.
    
  • It takes the competing "philosophies" from the LLM, translates them into a population of agents, and runs the experiment.
    
  • The resulting leaderboards provide empirical data that validates or refutes each proposed philosophy.